Data Fabric vs Data Warehouse: Differences, Practical Examples & How They Complement Each Other

Last Updated on: May 30th, 2023, Published on: May 30th, 2023
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A data warehouse is a type of data repository used to store large amounts of structured data from various data sources. On the other hand, a data fabric is a composable, flexible and scalable way to maximize the value of data in an organization.

Both data fabric and data warehouses are two concepts that are often used in the world of data management, and they serve different purposes and use cases.

In this blog, we will understand their key differences and benefits and it will help you make the right decision for your organization.

Table of contents #

  1. Understanding the basics: Data warehouse and data fabric
  2. Data fabric and data warehouse: What to remember before implementation?
  3. Data fabric vs data warehouse: Practical examples and use cases
  4. How data warehouse and data fabric complement each other for a unique architectural design?
  5. Comparing data fabric vs data warehouse: Uncovering the contrasts in a comparative table
  6. Bringing it all together
  7. Data fabric vs data warehouse: Related reads

Understanding the basics: Data warehouse and data fabric #

In this section, we will learn the basics of a data warehouse and a data fabric and how they’re suitable for your organization.

What is a data warehouse? #

A data warehouse is a large, centralized repository of data that is collected from various sources within an organization. The data in a data warehouse is cleaned, transformed, and loaded (ETL process) so it can be used for reporting and data analysis.

  • It provides a way to manage data from disparate sources, making it easier to run complex queries and reports.
  • However, data warehouses often require significant upfront design and ongoing maintenance.
  • Besides, they’re not always ideal for real-time data analysis because the data needs to be processed before it’s available.

What is data fabric? #

A data fabric is a more modern approach to data management that leverages technologies like machine learning and artificial intelligence. It creates a unified, integrated layer of data services that can be deployed across different environments.

  • Data fabric solutions provide capabilities like data discovery, data integration, data quality, and data security, and they can work with both structured and unstructured data.
  • The main advantage of a data fabric is that it can provide real-time, actionable insights from a wide variety of data sources without needing to move or transform the data first.
  • If you’re dealing with a high volume of data from various sources, and you need to provide real-time insights to multiple business teams, a data fabric might be a good fit. This could help you democratize data access while also maintaining strong data governance and security.

On the other hand, if your organization mainly relies on structured data and needs a robust solution for historical reporting and analysis, a data warehouse could still be a useful tool.

Data fabric and data warehouse: What to remember before implementation? #

Be it data fabric or data warehouse, here’re a few things to consider before implementing any of these:

  1. Define use cases
  2. Assess current data architecture
  3. Data governance
  4. Choose the right tools & technologies
  5. Pilot & scale

Let’s look into each of the above aspects in brief:

1. Define use cases #

Understand and document the needs and requirements of each team. This includes the type of data they need, the frequency of data access, and the level of data granularity required.

2. Assess current data architecture #

Review the current state of data architecture in your organization. Identify the sources of data, the formats, and the systems interacting with it.

3. Data governance #

Implement data governance policies. This includes defining who has access to what data, how data privacy will be maintained, and how data quality will be assured.

4. Choose the right tools & technologies #

Depending on whether you choose to implement a data warehouse, a data fabric, or a hybrid approach, you’ll need to select the appropriate tools and technologies. This may include ETL tools, data warehouse software, or data fabric platforms.

5. Pilot & scale #

Start with a pilot project involving a smaller subset of your data or a single business team. This will help you to understand potential challenges and to prove the value of the new approach before scaling it across the organization.

Remember that the goal is to empower your teams to make data-driven decisions, and the best solution will depend on the specific needs and constraints of your organization.

Data fabric vs data warehouse: Practical examples and use cases #

Now, let’s explore some typical use cases for both data fabrics and data warehouses:

Data warehouse use-cases #

1. Business intelligence reporting #

  • Companies use data warehouses to consolidate data from different sources for generating reports and insights.
  • For example, a retail company might pull data from its sales, customer, and inventory systems into a data warehouse to generate comprehensive reports about sales performance.

2. Historical data analysis #

  • Data warehouses are excellent for analyzing historical data.
  • For instance, an insurance company might analyze years of claims data in its data warehouse to identify trends and predict future claims.

3. Data mining #

  • Data warehouses can be used to identify patterns in large data sets.
  • An e-commerce company might use data mining techniques on its data warehouse to identify shopping patterns and develop personalized marketing campaigns.

Data fabric use-cases #

1. Real-time analytics #

  • Data fabrics can pull data from multiple sources in real-time, making them suitable for use cases that require up-to-the-minute data.
  • For instance, a financial institution might use a data fabric to monitor transactions from different systems in real-time to detect and prevent fraudulent activity.

2. Data democratization #

  • With data fabric, businesses can democratize data access across different teams while maintaining strong governance.
  • For example, a large manufacturing firm with multiple plants can use a data fabric to give plant managers access to production, quality, and supply chain data across different systems. This will enable them to make data-driven decisions quickly.

3. IoT and big data processing #

  • Data fabric’s ability to handle vast amounts of structured and unstructured data makes it suitable for big data and IoT use cases.
  • An energy company, for instance, could use a data fabric to ingest, process, and analyze data from millions of smart meters across a city in real-time to optimize energy consumption.

4. AI and ML model training #

  • Data scientists often need to access diverse data sets for training machine learning models.
  • A data fabric could be used to provide a unified view and access to these disparate data sources without needing to move or copy data around.
  • For example, a healthcare research institution could use a data fabric to pull together patient data, clinical trial data, genomic data, and more from various sources. This provides a rich, unified dataset for developing predictive models for disease diagnosis or treatment outcomes.

Remember, the choice between a data warehouse and a data fabric doesn’t have to be either/or; in many scenarios, they can complement each other.

How do data warehouses and data fabrics complement each other for a unique architectural design? #

Data warehouses and data fabrics can indeed complement each other in data architecture. The key is to understand that while they both serve the purpose of storing and managing data, they address different types of data needs and workloads.

In a complementary architecture:

  • A data warehouse would continue to handle the workloads it’s most suited for, such as structured reporting, historical analysis, and business intelligence tasks.
  • This usually involves structured, cleaned, and processed data that has been transformed via ETL (Extract, Transform, Load) processes for easy consumption by analytics tools and services.

On the other hand, a data fabric would handle the real-time, less structured data needs. This includes integrating data from various sources, managing real-time analytics, handling big data or IoT workloads, and providing a unified data access layer for diverse data types and sources.

Here’s how such an architecture could look:

  1. Data sources
  2. Data fabric layer
  3. ETL processes
  4. Data warehouse
  5. Analytics tools
  6. Data consumers

Now, let us look into each of the above aspects in detail:

1. Data sources #

You start with various data sources, such as transactional databases, log files, external APIs, IoT devices, etc.

2. Data fabric layer #

The data fabric sits next to these data sources, providing real-time access and integration. It handles data governance, data discovery, and data security across all these sources. It also serves real-time and big data analytics needs directly.

3. ETL processes #

For structured reporting and historical analysis needs, you run ETL processes to extract data from the various sources (or from the data fabric itself), transform the data into a suitable format, and load it into the data warehouse.

4. Data warehouse #

The data warehouse stores the cleaned and transformed data, ready for business intelligence tools to consume.

5. Analytics tools #

These tools can connect to both the data warehouse for structured reporting and historical analysis, and the data fabric for real-time and complex analytics needs. This allows for both scheduled, batch-style reporting and real-time, ad-hoc data exploration and analytics.

6. Data consumers #

Different teams in the organization can access the insights they need. This could be through dashboards and reports, or via APIs and other interfaces for more complex, programmatic data needs.

By using both a data fabric and a data warehouse, you can create a flexible, scalable, and powerful data architecture. This way, you can handle a wide variety of data workloads and needs, ensuring that different teams in your organization can access the data they need when they need it.

Comparing data fabric vs data warehouse: Uncovering the contrasts in a comparative table #

Now that we’ve learnt the basics of a data fabric and a data warehouse, let look at a comparison table to recollect what we discussed above:

CriteriaData WarehouseData Fabric
DefinitionCentralized storage for structured data from multiple sources.A unified, integrated layer of data services deployable across different environments.
Use CasesBI Reporting, Historical Data Analysis, Data Mining.Real-Time Analytics, Data Democratization, IoT & Big Data, AI/ML Model Training.
Data IntegrationUses ETL processes to integrate data.Direct integration from multiple sources in real-time.
Data ProcessingBatch processing, often not ideal for real-time insights.Real-time processing, ideal for up-to-the-minute insights.
Data StructurePrimarily structured data.Both structured and unstructured data.
Data AccessMore rigid access, often through predefined reports and dashboards.Flexible and democratized access to data.
SpeedGenerally slower due to the ETL process.Generally faster due to real-time processing.
ScalabilityCan be scaled but often requires significant resources and planning.Highly scalable, can handle big data and IoT workloads efficiently.
MaintenanceRequires significant upfront design and ongoing maintenance.AI and ML technologies can automate many tasks, reducing maintenance efforts.
Real-time CapabilityLimited. Data needs to be processed and loaded before being available.High. Can provide real-time, actionable insights.
Ideal ForConsolidating historical data for in-depth analysis and reporting.Providing real-time insights, handling big data workloads, and democratizing data access.
Role in ArchitectureActs as the primary storage for structured, cleaned, and processed data.Sits next to various data sources providing real-time access and integration.

Bringing it all together #

A data warehouse can provide a structured, curated view of important business data for reporting and analysis. On the other hand, a data fabric can help manage data across multiple systems, support real-time analytics, and facilitate a more democratized and flexible approach to data access and usage.

The key takeaway is that the choice between a data warehouse and a data fabric should be guided by your organization’s specific data needs and use cases, and in many scenarios, they can complement each other to provide a comprehensive data solution.

Remember that the choice isn’t always a binary one nor are these two approaches mutually exclusive - these two can complement each other in a data architecture.

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